code: def make_nn_regression(
      n_samples=100,
      n_features=100,
      n_informative=10,
      dense=False,
      noise=0.0,
      test_size=0,
      normalize_x=True,
      normalize_y=True,
      shuffle=True,
      random_state=None,
  ):
      X, y, w = _make_nn_regression(
          n_samples=n_samples,
          n_features=n_features,
          n_informative=n_informative,
          shuffle=shuffle,
          random_state=random_state,
      )
  
      if dense:
          X = X.toarray()
  
      if test_size > 0:
          cv = ShuffleSplit(
              len(y),
              n_iter=1,
              random_state=random_state,
              test_size=test_size,
              train_size=1 - test_size,
          )
  
          train, test = list(cv)[0]
          X_train, y_train = X[train], y[train]
          X_test, y_test = X[test], y[test]
          if not dense:
              X_train.sort_indices()
              X_test.sort_indices()
      else:
          X_train, y_train = X, y
          if not dense:
              X_train.sort_indices()
          X_test, y_test = None, None
  
      # Add noise
      if noise > 0.0:
          generator = check_random_state(random_state)
          y_train += generator.normal(
              scale=noise * np.std(y_train), size=y_train.shape
          )
          y_train = np.maximum(y_train, 0)
  
      if normalize_x:
          normalizer = Normalizer()
          X_train = normalizer.fit_transform(X_train)
          if X_test is not None:
              X_test = normalizer.transform(X_test)
  
      if normalize_y:
          scaler = MinMaxScaler()
          y_train = scaler.fit_transform(y_train.reshape(-1, 1)).ravel()
          if y_test is not None:
              y_test = scaler.transform(y_test.reshape(-1, 1)).ravel()
  
      if X_test is not None:
          return X_train, y_train, X_test, y_test, w
      else:
          return X_train, y_train, w
  
file_path: lightning/lightning/impl/datasets/samples_generator.py
imports: from itertools import product
  import numpy
  import scipy.sparse
  from sklearn.utils.extmath import safe_sparse_dot
  from sklearn.utils import check_random_state
  from sklearn.utils import shuffle
  from sklearn.preprocessing import MinMaxScaler
  from sklearn.preprocessing import Normalizer
label_desc: Use image node_id 2 for calling a global function with example usage: make_nn_regression(n_samples, n_features, n_informative, dense, noise, test_size, normalize_x, normalize_y, shuffle, random_state) and returns: X_train,  y_train,  X_test,  y_test,  w, X_train,  y_train,  w